Science, Engineering and Technology Proceedings
type: channel
description: An international, peer-reviewed open-access platform dedicated to publishing high-quality conference proceedings that explore the intersections of science, engineering, and technology, and their transformative impact on societal advancement. The series aims to provide a comprehensive forum for researchers, scholars, and practitioners to share their latest findings, foster interdisciplinary collaboration, and address global challenges. Each submission undergoes a rigorous peer-review process to ensure the publication of relevant, impactful, and timely content that contributes to advancing scientific knowledge, engineering practices, and technological innovations for the betterment of society.
publisher: George Brown Press
language: en
publication name: Science, Engineering and Technology Proceedings
issn: 3079-8612
items: The Role of AI and Machine Learning in Accelerating Drug Discovery and Personalized Medicine
items: Research on Deep Learning-Based Intelligent Prediction Models for Quality Deterioration in Grain and Oil Storage
items: A Review of Aircraft Aerodynamic and Structural Optimization Method Based on Artificial Intelligence
items: New Generation Drug Delivery Systems: Nanocarriers, Smart Hydrogels, and Organelle Targeting Technologies
items: Analyzing Protein Structures and Functions in Tube A & B from Borduria
items: Multi-Agent Reinforcement Learning for Autonomous Driving in Signal-Free Intersections and Roundabouts
items: Impact of Microbial Fermentation Engineering on Food Nutrition and Health-Promoting Properties
items: Dissecting the Mechanisms of Neuromuscular Junction Plasticity through a Single-Cell Multi-Omics Lens
items: Terahertz Perfect Absorption Based on Ultrathin Conductive Films
items: Contemporary Research on Interdisciplinary Professional Development Methods
items: Application of Chemical Stabilizers to Sustain Urease Inhibition in Soil–Plant Systems
items: Metal-Catalyzed Reactions for Energy and Biomedical Applications
items: Modern Management Approaches for Complex Interdisciplinary Systems
items: Data Analytics Applications in Diverse Professional Domains
items: Functional Coordination Polymers with Dual Catalytic Applications
items: Multi-Metal Catalytic Systems for Selective Product Formation
items: Structural Engineering of Metal Sites for Catalysis
items: Electrocatalytic and Enzymatic Inhibition by Metal Complexes
items: Enzyme Inhibitor Development Using Metal-Organic Framework Technology: Therapeutic and Agricultural Potential
items: Urease Inhibition Mechanisms in Soil-Plant Systems Using Coordination Polymers for Sustainable Agriculture
items: Design of Metal–Organic Frameworks with V-Shaped Auxiliary Ligands: Insights into Structure and Function
items: Independent Predictive Factors for Positive Margins after Conization of Cervical HSIL in Women of Childbearing Age and the Necessity of Secondary Surgery
items: Length-Weight Relationship Modeling Applications in Aquatic Species Biomass Estimation Methods
items: Migration Infrastructure Design Impacts on Riverine Crustacean Population Dynamics Studies
items: Carapace Measurement Techniques for Determining Growth Rates in Cultured Crustaceans
items: Freshwater Ecosystem Management Strategies for Commercially Important Decapod Species Populations
items: Crustacean Morphological Adaptations in Controlled Aquaculture Environments and Feeding Systems
items: Spatiotemporal Modeling of Soil Moisture in Humid Areas by Integrating Transformer Architecture and Remote Sensing Data
items: Leveraging NLP for Semantic and Numerical Inconsistency Detection in Tax Submissions
items: A Fault Diagnosis Method for the Charging and Discharging Water System of a Certain Ship Lift Based on Historical Data Analysis

The Role of AI and Machine Learning in Accelerating Drug Discovery and Personalized Medicine
type: item
description: Artificial intelligence (AI) and machine learning (ML) are revolutionizing drug discovery and personalized medicine by offering powerful tools for analyzing complex biological data, predicting drug efficacy, and tailoring treatments to individual patients. This review paper explores the multifaceted roles of AI and ML in these domains, encompassing target identification, drug design, clinical trial optimization, and patient stratification. We begin with a historical overview of AI applications in healthcare, followed by a detailed examination of core themes such as AI-driven drug repurposing and the use of ML in predictive diagnostics. The paper further delves into the challenges and limitations of implementing AI/ML technologies, including data bias, interpretability issues, and regulatory hurdles. Finally, we discuss future perspectives, emphasizing the potential of AI to transform healthcare through enhanced precision, accelerated drug development timelines, and improved patient outcomes. This review synthesizes current research, highlights key advancements, and identifies critical areas for future investigation, aiming to provide a comprehensive understanding of the transformative impact of AI and ML on drug discovery and personalized medicine. The successful integration of these technologies promises to unlock new possibilities for preventing and treating diseases, paving the way for a more proactive and patient-centric healthcare system.
subject: Articles
  topic: section
subject: artificial intelligence
  topic: keywords
subject: machine learning
  topic: keywords
subject: drug discovery
  topic: keywords
subject: personalized medicine
  topic: keywords
subject: healthcare
  topic: keywords
subject: drug repurposing
  topic: keywords
subject: predictive diagnostics
  topic: keywords
creator: Liang Xu
creator: Xiaoyu Zhang
rights: Copyright (c) 2025 Liang Xu, Xiaoyu Zhang (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-01-30
publication date: 2026-01-30
starting page: 76
ending page: 83
doi: 10.71222/v2qvxe86

Research on Deep Learning-Based Intelligent Prediction Models for Quality Deterioration in Grain and Oil Storage
type: item
description: Stored grain and oil quality are linked to a nation's food security and economic benefits. Traditional grain and oil monitoring methods are mostly performed by practitioners using their experience which can lead to poor timeliness and accuracy of information. Therefore, the purpose of this research is to use deep learning technology to build an intelligent predictive model which helps to overcome the issue of maintaining stored grain and oil quality in complicated storage conditions. The model uses multiple types of monitoring data such as temperature, humidity, and pictures to create an advanced quality deterioration early warning system that can support many different practical storage scenarios. Findings from this research indicate that the model can successfully analyze the complex nonlinear relationships among many of the critical components that affect quality. The findings provide a new technical approach for the intelligent and proactive management of grain oil storage quality.
subject: Articles
  topic: section
subject: deep learning
  topic: keywords
subject: grain and oil storage
  topic: keywords
subject: quality deterioration
  topic: keywords
subject: intelligent prediction
  topic: keywords
subject: model
  topic: keywords
creator: Siyi Li
rights: Copyright (c) 2025 Siyi Li (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-01-03
publication date: 2026-01-03
starting page: 69
ending page: 75
doi: 10.71222/jwetf723

A Review of Aircraft Aerodynamic and Structural Optimization Method Based on Artificial Intelligence
type: item
description: With aviation technology continuous development, aircraft design is going toward faster, lighter and more intelligent direction. Aerodynamic shape characteristic and structural stiffness are two very important factor that show aircraft overall performance, so optimize aerodynamic shape and structural stiffness always is hot research direction in aviation system engineering field. Traditional aerodynamic-structure optimization design method usually need consume many CFD and FEA large numerical simulation, and need bigger design freedom and higher design efficiency, hard to fit modern complex aviation system design process. These years, machine learning, deep learning, reinforcement learning and evolutionary algorithm etc. artificial intelligence technology develop very rapid, bring completely new idea for aircraft optimization design, can on data-driven base, through machine learning, deep learning, reinforcement learning and evolutionary algorithm etc. way complete aircraft aerodynamic characteristic prediction, aircraft structural response modeling, based on aircraft aerodynamic and structure multi-objective optimization model global optimization etc. task, thus can effective reduce aircraft design high calculation cost and improve design accuracy. This paper review machine learning, deep learning, reinforcement learning and evolutionary algorithm etc. artificial intelligence technology in aviation aircraft aerodynamic and structural optimization design development and future use technical characteristic, key introduce surrogate model building, through deep neural network do aircraft flow field prediction and shape reconstruction, based on reinforcement learning adaptive control and multi-objective optimization, evolutionary algorithm in aircraft lightweight design application etc. research method, discuss AI technology in aviation aircraft aerodynamic-structure integrated optimization design and multidisciplinary collaborative design application prospect and chall…
subject: Articles
  topic: section
subject: artificial intelligence
  topic: keywords
subject: aircraft design
  topic: keywords
subject: aerodynamic optimiztion
  topic: keywords
subject: structural optimization
  topic: keywords
creator: Haoran Tang
rights: Copyright (c) 2025 Haoran Tang (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-01-03
publication date: 2026-01-03
starting page: 59
ending page: 68
doi: 10.71222/c650ax61

New Generation Drug Delivery Systems: Nanocarriers, Smart Hydrogels, and Organelle Targeting Technologies
type: item
description: The field of drug delivery has undergone significant advancements with the emergence of novel technologies such as nanocarriers, smart hydrogels, and organelle-targeting systems. These innovations enable more precise, efficient, and controlled delivery of therapeutic agents, enhancing drug efficacy while minimizing side effects. Nanocarriers, including lipid nanoparticles and polymeric nanoparticles, improve drug solubility and stability, allowing for controlled release and targeted delivery to specific tissues or cells. Smart hydrogels, responsive to environmental stimuli like pH and temperature, offer sustained drug release and site-specific targeting, making them ideal for chronic disease treatment. Organelle-targeting technology, which directs drugs to cellular organelles such as mitochondria and lysosomes, has emerged as a cutting-edge approach to precision medicine. These technologies hold the potential to address challenges like poor drug solubility, non-specific distribution, and treatment resistance. However, challenges remain, including biocompatibility, long-term stability, and scalability. Future research will focus on overcoming these hurdles and integrating these technologies to create personalized and efficient drug delivery systems. The development of multi-functional, hybrid systems combining nanocarriers, hydrogels, and organelle targeting will pave the way for more effective, targeted therapies.
subject: Articles
  topic: section
subject: drug delivery
  topic: keywords
subject: nanocarriers
  topic: keywords
subject: smart hydrogels
  topic: keywords
subject: organelle targeting
  topic: keywords
subject: precision medicine
  topic: keywords
creator: Zhichao Wang
rights: Copyright (c) 2025 Zhichao Wang (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-01-03
publication date: 2026-01-03
starting page: 49
ending page: 58
doi: 10.71222/btmzw826

Analyzing Protein Structures and Functions in Tube A & B from Borduria
type: item
description: During a routine retrieval operation at a remote research station, a field technician collected two unidentified laboratory sample tubes, labeled A and B, for subsequent scientific evaluation. These samples were believed to originate from an ongoing experimental project whose details had not been fully documented, and their analysis was expected to clarify the project’s technical focus and research direction. While the technician was returning with the materials, Tube B was accidentally damaged, resulting in the loss of part of its contents and a brief exposure incident. The technician later developed acute symptoms and was unable to provide further information about the circumstances surrounding the spill. This event increased the urgency of examining the remaining Tube A to determine the nature, stability, and potential laboratory relevance of the preserved sample. The incident also underscored the importance of traceable sample management, standardized handling procedures, and careful cross-disciplinary analysis when dealing with unknown research materials. The following study provides a systematic evaluation of the specimen in Tube A, aiming to identify structural features, possible biological origins, and any functional properties that may be relevant to ongoing scientific work.
subject: Articles
  topic: section
subject: protein structure analysis
  topic: keywords
subject: biochemical exposure
  topic: keywords
subject: hazardous compounds
  topic: keywords
subject: experimental sample characterization
  topic: keywords
subject: intelligence investigation
  topic: keywords
subject: cross-disciplinary laboratory evaluation
  topic: keywords
creator: Xinjue Zhou
rights: Copyright (c) 2025 Xinjue Zhou (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-12-20
publication date: 2025-12-20
starting page: 41
ending page: 48
doi: 10.71222/gtrfze36

Multi-Agent Reinforcement Learning for Autonomous Driving in Signal-Free Intersections and Roundabouts
type: item
description: Signal-Free Intersections & Roundabouts: One of the most difficult problems that a self-driving car faces is the proper functioning at intersections with no traffic lights. It is because there are no traffic lights, which assist us in making quick decisions, unlike just using lidar sensors and cameras for fixed-lane driving. In these situations the current research on multi-agent reinforcement learning (MARL) does not extent to such domains because of issues like non-stationarity and partial observability. The novel hierarchical learning framework for related communications integrates hierarchical learning structures and complex cost functions, which can better adjust a more adaptive response to safety and traffic flow efficiency.
subject: Articles
  topic: section
subject: signal-free intersections
  topic: keywords
subject: MARL
  topic: keywords
subject: LCF
  topic: keywords
creator: Haoran Tang
rights: Copyright (c) 2025 Haoran Tang (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-12-20
publication date: 2025-12-20
starting page: 32
ending page: 40
doi: 10.71222/ymabk538

Impact of Microbial Fermentation Engineering on Food Nutrition and Health-Promoting Properties
type: item
description: Microbial fermentation engineering plays a critical role in enhancing the nutritional quality and functional properties of foods. This review summarizes recent advances in fermentation technologies and their impacts on macronutrients, micronutrients, and anti-nutritional factors, highlighting improvements in protein digestibility, amino acid composition, lipid profiles, vitamin content, and mineral bioavailability. Additionally, fermentation promotes the production of bioactive compounds, including peptides, polyphenols, exopolysaccharides, and gamma-aminobutyric acid (GABA), which contribute to antioxidative, anti-inflammatory, and metabolic health benefits. Probiotic microorganisms in fermented foods support gut health, immune modulation, and metabolic regulation, while fermentation also enhances food safety by reducing mycotoxins, biogenic amines, and pathogenic microbes. The review further discusses current challenges, including variability in microbial metabolism, standardization issues, and regulatory considerations, and highlights the potential of synthetic biology, multi-omics, and predictive fermentation strategies for next-generation functional foods. Overall, microbial fermentation engineering offers a versatile and sustainable approach to improving human nutrition and health, bridging traditional practices with modern biotechnological innovations.
subject: Articles
  topic: section
subject: microbial fermentation
  topic: keywords
subject: functional foods
  topic: keywords
subject: probiotics
  topic: keywords
subject: bioactive compounds
  topic: keywords
subject: food nutrition
  topic: keywords
creator: Siyi Li
rights: Copyright (c) 2025 Siyi Li (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-12-20
publication date: 2025-12-20
starting page: 21
ending page: 31
doi: 10.71222/etfjtg11

Dissecting the Mechanisms of Neuromuscular Junction Plasticity through a Single-Cell Multi-Omics Lens
type: item
description: The neuromuscular junction (NMJ) serves as the fundamental interface between motor neurons and skeletal muscles, enabling precise motor control. Its structural and functional plasticity underlies muscle regeneration, motor learning, and recovery from injury. Traditional approaches relying on histology and bulk omics have provided valuable insights into NMJ structure but fail to capture cell-type heterogeneity, temporal dynamics, and spatial coordination during regeneration. A systems-level understanding of NMJ plasticity remains limited. This study integrates single-cell RNA sequencing, proteomic, and spatial transcriptomic data to construct a multidimensional framework for NMJ plasticity. Analytical methods include clustering, trajectory inference, ligand-receptor network modeling, and cross-omic module identification, interpreted under the Neuro-Muscular Plasticity Systems Framework (NMPSF). The analysis reveals specialized Schwann cell subtypes, bidirectional neuron-muscle communication through neuregulin-ErbB and agrin-MuSK pathways, and spatially organized extracellular matrix remodeling supporting reinnervation. Integrated multi-omic networks further identify molecular modules governing synaptic stability, regeneration, and immune-ECM interaction. These findings redefine NMJ plasticity as a coordinated, multi-level process linking molecular signaling, cellular diversity, and spatial organization. The proposed NMPSF offers a reproducible analytical and conceptual framework for studying neuromuscular adaptation and guiding regenerative interventions for neuromuscular disorders.
subject: Articles
  topic: section
subject: neuromuscular junction (NMJ)
  topic: keywords
subject: single-cell multi-omics
  topic: keywords
subject: schwann cells
  topic: keywords
subject: synaptic plasticity
  topic: keywords
subject: regenerative biology
  topic: keywords
creator: Mingyang Gao
rights: Copyright (c) 2025 Mingyang Gao (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-12-20
publication date: 2025-12-20
starting page: 10
ending page: 20
doi: 10.71222/xamkfm86

Terahertz Perfect Absorption Based on Ultrathin Conductive Films
type: item
description: Electromagnetic absorption is one of the most fundamental forms of light-matter interaction, spanning a broad spectral range from radio frequency to the ultraviolet band. In the terahertz (THz) regime, however, conventional materials typically exhibit weak absorption, which limits the performance of high-efficiency THz devices such as detectors, modulators, and sensors. The development of ultrathin conductive films (UTCFs), especially two-dimensional materials, offers new opportunities for enhancing THz absorption due to their unique optical and electrical characteristics. Yet, the extremely small thickness of these UTCFs often restricts their ability to sustain strong interaction with incident THz waves, making additional strategies necessary to achieve substantial absorption. In this work, using graphene as a representative UTCF, we demonstrate that near-perfect THz absorption can be realized through a total internal reflection (TIR) configuration. When the refractive index of the superstrate exceeds that of the substrate, TIR occurs once the incident angle surpasses the critical angle, enabling the confined evanescent field to interact strongly with the ultrathin film. This mechanism leads to highly efficient THz absorption in UTCF-based structures and supports a broadband and wide-angle near-perfect absorption region around the optimal absorption point. A detailed parameter-dependence analysis is conducted for both the UTCF and the surrounding dielectric environment, illustrating how the absorption peak evolves with material conductivity, film thickness, refractive-index contrast, and incidence angle. The findings provide a refined physical understanding and a practical design pathway for developing strong THz absorbers based on ultrathin conductive materials, contributing to future applications in THz sensing, imaging, and integrated photonic systems.
subject: Articles
  topic: section
subject: perfect absorption
  topic: keywords
subject: terahertz
  topic: keywords
subject: ultrathin conductive films
  topic: keywords
subject: total internal reflection
  topic: keywords
creator: KeLiang Luo
rights: Copyright (c) 2025 KeLiang Luo (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-12-20
publication date: 2025-12-20
starting page: 1
ending page: 9
doi: 10.71222/s36erb84

Contemporary Research on Interdisciplinary Professional Development Methods
type: item
description: Contemporary professional development increasingly demands interdisciplinary approaches that transcend traditional sectoral boundaries. This paper examines emerging methods and frameworks for enhancing professional competencies across diverse fields, with particular emphasis on technology-driven efficiency, project management innovation, and post-pandemic professional adaptation. The research investigates how digital transformation initiatives, collaborative learning environments, and competency-based training models contribute to workforce advancement in rapidly evolving industries. Through systematic analysis of contemporary practices, this study identifies critical success factors including technological integration, cross-functional collaboration, and adaptive learning strategies. The findings reveal that effective professional development programs incorporate blended learning modalities, real-time performance analytics, and industry-specific customization while maintaining transferable core competencies. This research contributes to understanding how organizations can design and implement sustainable professional development systems that respond to changing market demands, technological disruptions, and evolving workforce expectations. The implications extend to policy formulation, organizational strategy, and educational curriculum design for professional training programs.
subject: Articles
  topic: section
subject: professional development
  topic: keywords
subject: interdisciplinary learning
  topic: keywords
subject: technology integration
  topic: keywords
subject: workplace learning
  topic: keywords
subject: digital transformation
  topic: keywords
subject: competency framework
  topic: keywords
creator: Ming Gao
creator: Jie Huang
creator: Rui Qian
rights: Copyright (c) 2025 Ming Gao, Jie Huang, Rui Qian (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-10-28
publication date: 2025-10-28
starting page: 77
ending page: 85
doi: 10.71222/pwcqsw81

Application of Chemical Stabilizers to Sustain Urease Inhibition in Soil–Plant Systems
type: item
description: Nitrogen fertilizers play a crucial role in enhancing crop yield and quality in agricultural production. However, urea, as a major nitrogen source, is rapidly hydrolyzed by urease in soil, leading to nitrogen loss through ammonia volatilization or nitrate leaching. This not only reduces fertilizer use efficiency but also increases environmental pollution. Urease inhibitors can delay urea decomposition and improve nitrogen utilization, but their activity in soil is often rapidly diminished due to temperature, moisture, and microbial activity, limiting their long-term application. The introduction of chemical stabilizers provides a new strategy to prolong the effective period of urease inhibitors. In this study, urea and urease inhibitors were applied in a simulated soil–plant system, with the addition of different types of chemical stabilizers. The effects on urease activity, soil nitrogen dynamics, and plant growth and nitrogen uptake were systematically evaluated. The results showed that chemical stabilizers significantly slowed the degradation of urease inhibitors, maintaining low soil urease activity for an extended period. Correspondingly, ammonia volatilization was markedly reduced, and nitrate retention increased, indicating effective control of soil nitrogen loss. Meanwhile, plant nitrogen uptake and growth performance were improved, demonstrating that stabilizers can not only prolong the inhibitory effect but also promote crop growth. Mechanistic analysis suggested that chemical stabilizers may extend inhibitor activity by forming protective complexes with the inhibitors or by reducing microbial degradation rates. This strategy has significant potential for agricultural applications, as it can improve nitrogen use efficiency, reduce environmental pollution, and provide a theoretical basis for optimizing soil–plant nitrogen management. Future studies could further explore the effectiveness and applicability under different soil types, crop species, and long-t…
subject: Articles
  topic: section
subject: Urease inhibitors
  topic: keywords
subject: chemical stabilizers
  topic: keywords
subject: nitrogen retention
  topic: keywords
subject: soil–plant interaction
  topic: keywords
creator: Alexander Carter
rights: Copyright (c) 2025 Alexander Carter (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-10-27
publication date: 2025-10-27
starting page: 70
ending page: 76
doi: 10.71222/ejjrte30

Metal-Catalyzed Reactions for Energy and Biomedical Applications
type: item
description: Metal-catalyzed reactions have emerged as cornerstone technologies bridging energy conversion systems and biomedical therapeutics through their remarkable selectivity, efficiency, and tunability. This review examines the dual convergence of metal catalysis in addressing critical challenges in sustainable energy production and advanced healthcare delivery. The discussion encompasses single-atom catalysts, dual-metal site architectures, and coordination polymer frameworks that enable transformative processes including electrocatalytic carbon dioxide reduction, fuel cell oxygen reduction, hydrogen evolution, and targeted therapeutic interventions. Recent advances in biohybrid catalysis and metal-organic frameworks have demonstrated unprecedented control over reaction pathways and biological interactions, creating opportunities for integrated solutions to energy sustainability and human health challenges. Through analysis of catalyst design principles, mechanistic understanding, and structure-activity relationships, this work illustrates how strategic manipulation of metal centers, ligand environments, and support materials drives performance optimization across both domains. The synthesis of knowledge from energy catalysis and catalytic biomedicine reveals common design strategies and emerging opportunities for cross-disciplinary innovation that will shape future development of multifunctional metal-based systems capable of addressing interconnected global challenges in climate, energy security, and healthcare accessibility.
subject: Articles
  topic: section
subject: metal catalysis
  topic: keywords
subject: energy conversion
  topic: keywords
subject: electrocatalysis
  topic: keywords
subject: biomedical applications
  topic: keywords
subject: single-atom catalysts
  topic: keywords
subject: coordination polymers
  topic: keywords
creator: Jessica Lin
creator: Michae Chen
rights: Copyright (c) 2025 Jessica Lin, Michae Chen (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-10-27
publication date: 2025-10-27
starting page: 61
ending page: 69
doi: 10.71222/akkwke66

Modern Management Approaches for Complex Interdisciplinary Systems
type: item
description: Complex interdisciplinary systems present unprecedented management challenges requiring innovative approaches that transcend traditional project management paradigms. This paper examines contemporary management methodologies applicable across diverse domains including project evaluation frameworks, leadership practices, and performance optimization strategies. The investigation reveals that effective management of complex systems demands multidimensional evaluation criteria, adaptive leadership competencies, and integration of digital technologies with human expertise. Through systematic analysis of management practices in construction, software development, manufacturing, and professional training contexts, this study identifies convergent principles applicable across disciplinary boundaries. The findings demonstrate that successful management of interdisciplinary systems requires balance between standardized processes and contextual adaptation, emphasis on stakeholder engagement alongside technical excellence, and integration of quantitative metrics with qualitative assessment. Comparative analyses of leadership behaviors, project success factors, and performance evaluation methodologies reveal that contemporary management approaches prioritize flexibility, continuous learning, and collaborative problem-solving over rigid adherence to predetermined plans. These insights contribute to understanding how management frameworks can effectively address complexity, uncertainty, and rapid change characterizing modern interdisciplinary systems across professional domains.
subject: Articles
  topic: section
subject: project management
  topic: keywords
subject: interdisciplinary systems
  topic: keywords
subject: leadership competencies
  topic: keywords
subject: performance evaluation
  topic: keywords
subject: complex systems management
  topic: keywords
subject: organizational effectiveness
  topic: keywords
creator: Yang Liu
creator: Wei Zhang
rights: Copyright (c) 2025 Yang Liu, Wei Zhang (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-10-26
publication date: 2025-10-26
starting page: 50
ending page: 60
doi: 10.71222/b0t1qx81

Data Analytics Applications in Diverse Professional Domains
type: item
description: Data analytics has emerged as a transformative force across diverse professional domains, fundamentally altering decision-making processes and operational methodologies. This paper examines the application of analytical frameworks in multiple sectors including business intelligence, manufacturing systems, construction management, and digital platforms. The investigation reveals that analytics-driven approaches enable organizations to extract actionable insights from complex datasets, optimize performance metrics, and enhance strategic planning capabilities. Through systematic analysis of implementation strategies across various professional contexts, this study identifies common challenges including data quality issues, analytical skill gaps, and organizational resistance to data-driven paradigms. The findings demonstrate that successful analytics adoption requires integration of technological infrastructure with human expertise and organizational culture transformation. Comparative analyses of analytics applications in domains such as supply chain optimization, user engagement enhancement, risk assessment, and performance evaluation reveal convergent methodologies that transcend disciplinary boundaries. These insights contribute to understanding how data-driven decision-making frameworks can be adapted and scaled across different professional environments, providing practical guidance for organizations seeking to leverage analytics capabilities for competitive advantage and operational excellence.
subject: Articles
  topic: section
subject: data analytics
  topic: keywords
subject: business intelligence
  topic: keywords
subject: predictive analytics
  topic: keywords
subject: performance optimization
  topic: keywords
subject: data-driven decision-making
  topic: keywords
subject: organizational performance
  topic: keywords
creator: Priya Singh
creator: Daniel Wang
creator: Marie Dubois
rights: Copyright (c) 2025 Priya Singh, Daniel Wang, Marie Dubois (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-10-25
publication date: 2025-10-25
starting page: 40
ending page: 49
doi: 10.71222/zzvv4694

Functional Coordination Polymers with Dual Catalytic Applications
type: item
description: Functional coordination polymers have emerged as remarkable materials exhibiting dual catalytic capabilities that enable simultaneous or sequential transformations in complex reaction systems. These crystalline porous materials, constructed from metal ions or clusters connected by organic linkers, possess unique structural features including high surface areas, tunable pore environments, and diverse metal coordination sites that facilitate multiple catalytic functions. The integration of different catalytic functionalities within a single framework enables cascade reactions, tandem transformations, and multifunctional catalytic processes that would be difficult to achieve using conventional catalysts. This review examines the design principles underlying dual-functional coordination polymers, emphasizing how structural architecture, metal center selection, and ligand design contribute to multifunctional catalytic behavior. The synthetic strategies for creating coordination polymers with controlled dual functionalities are discussed, along with characterization approaches that reveal structure-activity relationships. Applications in various catalytic processes including organic transformations, environmental remediation, electrochemical reactions, and biomimetic catalysis are comprehensively analyzed. Special attention is given to systems demonstrating synergistic effects where dual functionalities enhance overall catalytic performance beyond simple additive contributions. The challenges associated with maintaining structural stability during catalytic processes and strategies for optimizing dual catalytic activities are addressed. This comprehensive analysis demonstrates that functional coordination polymers represent a powerful platform for developing advanced catalytic systems with multiple integrated functionalities.
subject: Articles
  topic: section
subject: coordination polymers
  topic: keywords
subject: dual catalysis
  topic: keywords
subject: metal-organic frameworks
  topic: keywords
subject: heterogeneous catalysis
  topic: keywords
subject: multifunctional materials
  topic: keywords
subject: tandem reactions
  topic: keywords
creator: Ming Patel
creator: Sofia Zhang
creator: Hiroshi Tanaka
rights: Copyright (c) 2025 Ming Patel, Sofia Zhang, Hiroshi Tanaka (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-10-25
publication date: 2025-10-25
starting page: 30
ending page: 39
doi: 10.71222/zvrb0y95

Multi-Metal Catalytic Systems for Selective Product Formation
type: item
description: Multi-metal catalytic systems have emerged as powerful platforms for achieving selective product formation in various chemical transformations. These systems leverage synergistic interactions between different metal centers to enhance catalytic activity, improve selectivity, and enable reactions that are challenging for single-metal catalysts. The strategic combination of metals creates unique electronic and geometric properties that facilitate targeted reaction pathways while suppressing undesired side reactions. This review examines the fundamental principles governing multi-metal catalysis, including electronic effects, geometric modifications, and cooperative mechanisms between metal sites. The application of bimetallic and trimetallic catalysts in hydrogenation reactions, carbon dioxide reduction, biomass conversion, and other industrially relevant processes is comprehensively discussed. Special attention is given to the design strategies that enable precise control over product distribution, including support selection, metal composition optimization, and surface engineering approaches. Recent advances in characterization techniques have provided deeper insights into the structure-activity relationships in multi-metal systems, facilitating rational catalyst design. The challenges associated with maintaining selectivity under various reaction conditions and strategies for catalyst stability enhancement are also addressed. This comprehensive analysis demonstrates that multi-metal catalytic systems represent a promising avenue for developing sustainable and efficient chemical processes with enhanced selectivity.
subject: Articles
  topic: section
subject: multi-metal catalysts
  topic: keywords
subject: selective catalysis
  topic: keywords
subject: bimetallic systems
  topic: keywords
subject: synergistic effects
  topic: keywords
subject: product selectivity
  topic: keywords
subject: catalytic transformations
  topic: keywords
creator: David Chen
creator: Yuki Nakamura
rights: Copyright (c) 2025 David Chen, Yuki Nakamura (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-10-24
publication date: 2025-10-24
starting page: 20
ending page: 29
doi: 10.71222/qd0ftn57

Structural Engineering of Metal Sites for Catalysis
type: item
description: Structural engineering of metal sites has emerged as a pivotal strategy for advancing heterogeneous catalysis, enabling unprecedented control over catalytic activity, selectivity, and stability. The precise manipulation of metal coordination environments, metal-support interactions, and spatial arrangements of active sites provides opportunities to tailor catalytic properties for specific reactions. Single-atom catalysts represent a frontier in this field, offering maximum atomic efficiency and unique electronic structures that differ fundamentally from nanoparticles and bulk materials. The coordination geometry, oxidation state, and local environment of isolated metal atoms critically determine their catalytic performance in reactions ranging from electrocatalytic carbon dioxide reduction to hydrogen peroxide synthesis. Dual-metal site architectures introduce additional complexity and synergy, enabling tandem catalytic processes and cooperative substrate activation. This paper examines the principles and strategies of structural engineering for metal catalytic sites, exploring the relationships between atomic-scale structure and catalytic function. Recent advances in synthesis methods, characterization techniques, and theoretical understanding have enabled rational design of metal sites with optimized properties. The integration of experimental observations with computational predictions continues to drive progress in developing next-generation catalysts for energy conversion, environmental remediation, and chemical synthesis applications.
subject: Articles
  topic: section
subject: single-atom catalysts
  topic: keywords
subject: metal-support interactions
  topic: keywords
subject: coordination engineering
  topic: keywords
subject: heterogeneous catalysis
  topic: keywords
subject: structural design
  topic: keywords
subject: catalytic sites
  topic: keywords
creator: Jian Liu
creator: Anna Rodriguez
creator: Kenji Yamamoto
rights: Copyright (c) 2025 Jian Liu, Anna Rodriguez, Kenji Yamamoto (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-10-24
publication date: 2025-10-24
starting page: 10
ending page: 19
doi: 10.71222/9z5d7d54

Electrocatalytic and Enzymatic Inhibition by Metal Complexes
type: item
description: Metal complexes have emerged as versatile compounds with significant applications in both electrocatalysis and enzymatic inhibition. The unique electronic properties and coordination geometries of transition metal complexes enable them to facilitate electron transfer processes in electrocatalytic reactions while simultaneously interacting with biological enzymes to modulate their activity. This paper explores the dual functionality of metal complexes in advancing electrocatalytic processes for energy conversion and environmental remediation, as well as their therapeutic potential through enzyme inhibition mechanisms. The coordination environment, oxidation states, and ligand architectures play crucial roles in determining the efficacy of metal complexes in both domains. Recent developments in copper-based coordination polymers, ruthenium catalysts, and zinc-containing metalloenzyme inhibitors demonstrate the growing importance of rational design strategies. Understanding the structure-activity relationships and mechanistic pathways is essential for developing next-generation catalysts and therapeutic agents. This review examines the current state of metal complex applications, highlighting key design principles, mechanistic insights, and future directions for research in this interdisciplinary field spanning materials chemistry, bioinorganic chemistry, and medicinal chemistry.
subject: Articles
  topic: section
subject: metal complexes
  topic: keywords
subject: electrocatalysis
  topic: keywords
subject: enzyme inhibition
  topic: keywords
subject: coordination polymers
  topic: keywords
subject: transition metals
  topic: keywords
subject: bioinorganic chemistry
  topic: keywords
creator: Sarah Kim
rights: Copyright (c) 2025 Sarah Kim (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-10-23
publication date: 2025-10-23
starting page: 1
ending page: 9
doi: 10.71222/ah44z809

Enzyme Inhibitor Development Using Metal-Organic Framework Technology: Therapeutic and Agricultural Potential
type: item
description: Metal-organic frameworks (MOFs) have emerged as revolutionary platforms for enzyme inhibitor development, offering unprecedented opportunities in both therapeutic and agricultural applications. These crystalline porous materials, composed of metal nodes connected by organic linkers, provide unique structural properties that enable precise control over enzyme-inhibitor interactions. The high surface area, tunable pore sizes, and customizable chemical environments of MOFs facilitate the design of highly selective and efficient enzyme inhibitors. In therapeutic applications, MOF-based enzyme inhibitors show remarkable potential for treating various diseases by targeting specific enzymatic pathways while minimizing off-target effects. Agricultural applications demonstrate significant promise in developing environmentally sustainable enzyme inhibitors for pest control and crop protection. The versatility of MOF platforms allows for the incorporation of multiple inhibitory mechanisms within a single framework, enhancing overall efficacy. Recent advances in MOF synthesis and functionalization have enabled the development of stimuli-responsive inhibitor systems that can be activated under specific conditions. This comprehensive review examines the current state of MOF-based enzyme inhibitor development, highlighting key breakthroughs in design strategies, synthesis methodologies, and practical applications. The integration of computational modeling with experimental validation has accelerated the discovery of novel inhibitor-MOF combinations with enhanced selectivity and potency.
subject: Articles
  topic: section
subject: metal-organic frameworks
  topic: keywords
subject: enzyme inhibitors
  topic: keywords
subject: therapeutic applications
  topic: keywords
subject: agricultural biotechnology
  topic: keywords
subject: drug delivery
  topic: keywords
subject: biosensors
  topic: keywords
creator: Liang Chen
rights: Copyright (c) 2025 Liang Chen (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-10-16
publication date: 2025-10-16
starting page: 79
ending page: 88
doi: 10.71222/5fgxms43

Urease Inhibition Mechanisms in Soil-Plant Systems Using Coordination Polymers for Sustainable Agriculture
type: item
description: The increasing demand for sustainable agricultural practices has intensified research into enzyme inhibition mechanisms that can optimize nitrogen utilization efficiency in soil-plant systems. Urease, a critical enzyme responsible for urea hydrolysis in soils, plays a pivotal role in nitrogen cycling but often leads to significant nitrogen losses through ammonia volatilization when not properly regulated. This study investigates the application of coordination polymers as innovative urease inhibitors in soil-plant systems, focusing on their mechanisms of action, environmental stability, and agricultural implications. Coordination polymers, characterized by their unique structural properties and tunable chemical compositions, offer promising solutions for controlled enzyme inhibition while maintaining soil health and supporting plant growth. The research examines various copper-based coordination polymers and their effectiveness in prolonging urease inhibition compared to conventional chemical stabilizers. Results demonstrate that coordination polymers exhibit superior performance in maintaining enzyme inhibition over extended periods, with minimal adverse effects on beneficial soil microorganisms and plant development. The study also evaluates the impact of these inhibitors on soil carbon, nitrogen, and phosphorus dynamics, revealing enhanced nutrient retention and improved fertilizer use efficiency. Furthermore, the investigation explores the relationship between coordination polymer structure and inhibition selectivity, providing insights into the design of next-generation agricultural amendments. These findings contribute to the development of environmentally sustainable approaches to nitrogen management in agricultural systems, offering potential solutions to reduce greenhouse gas emissions while maintaining crop productivity.
subject: Articles
  topic: section
subject: urease inhibition
  topic: keywords
subject: coordination polymers
  topic: keywords
subject: soil enzymes
  topic: keywords
subject: sustainable agriculture
  topic: keywords
subject: nitrogen cycling
  topic: keywords
subject: soil health
  topic: keywords
creator: James Hu
rights: Copyright (c) 2025 James Hu (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-10-15
publication date: 2025-10-15
starting page: 69
ending page: 78
doi: 10.71222/ecehaf67

Design of Metal–Organic Frameworks with V-Shaped Auxiliary Ligands: Insights into Structure and Function
type: item
description: Metal-organic frameworks (MOFs) represent a revolutionary class of crystalline materials that have garnered significant attention due to their exceptional structural diversity and multifunctional properties. The incorporation of V-shaped auxiliary ligands in MOF design has emerged as a powerful strategy for controlling structural architecture and enhancing functional capabilities. This comprehensive review examines the role of V-shaped ligands in directing the formation of three-dimensional hybrid networks, influencing pore characteristics, and enabling specific applications ranging from gas adsorption to catalysis and drug delivery. The geometric constraints imposed by V-shaped ligands provide unique opportunities for engineering breathing behaviors, flexibility, and selective binding properties. Recent advances demonstrate that strategic use of V-shaped auxiliary ligands significantly improves thermal stability, enhances sorption properties, and creates highly efficient catalytic systems. Furthermore, these ligands enable MOFs with specialized functionalities, including electrochromic properties, supercapacitor applications, and enzyme inhibition capabilities. This review synthesizes current understanding of structure-property relationships in V-shaped ligand-based MOFs, highlighting key design principles and emerging applications.
subject: Articles
  topic: section
subject: metal-organic frameworks
  topic: keywords
subject: V-shaped ligands
  topic: keywords
subject: structural architecture
  topic: keywords
subject: breathing behavior
  topic: keywords
subject: catalysis
  topic: keywords
subject: gas adsorption
  topic: keywords
creator: Yang Chen
rights: Copyright (c) 2025 Yang Chen (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-10-15
publication date: 2025-10-15
starting page: 60
ending page: 68
doi: 10.71222/92zyb239

Independent Predictive Factors for Positive Margins after Conization of Cervical HSIL in Women of Childbearing Age and the Necessity of Secondary Surgery
type: item
description: This study aims to analyze the high-risk factors for positive surgical margins after loop electrosurgical excision procedure (LEEP) and the necessity of secondary surgery in women of childbearing age (20-40 years) with high-grade squamous intraepithelial lesions (HSIL). Through retrospective analysis of 526 HSIL patients who underwent LEEP at the Third Affiliated Hospital of Sun Yat-sen University from 2015 to 2019, the positive margin rate was found to be 22.43%. Multivariate analysis indicated that TCT result of HSIL (OR = 3.414, 95%CI = 1.793-6.500, P < 0.001) and lesions involving ≥5 points (OR = 8.567, 95%CI = 4.439-16.533, P < 0.001) were independent risk factors for positive margins. Among 34 patients who underwent secondary surgery within six months after initial conization, 32 had positive margins, and the residual disease rate among them was only 21.88% (7/32), indicating that positive margins do not necessarily lead to residual lesions. Therefore, the decision for secondary surgery in patients with positive margins should be individualized based on factors such as age, fertility needs, lesion characteristics, complications, and personal preferences, rather than reflexively performing secondary surgery.
subject: Articles
  topic: section
subject: women of childbearing age
  topic: keywords
subject: cervical high-grade squamous intraepithelial lesion
  topic: keywords
subject: loop electrosurgical excision procedure
  topic: keywords
subject: positive margin
  topic: keywords
subject: predictive factors
  topic: keywords
subject: secondary surgery
  topic: keywords
creator: Yingying Huang
rights: Copyright (c) 2025 Yingying Huang (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-09-30
publication date: 2025-09-30
starting page: 53
ending page: 59
doi: 10.71222/veqxbw68

Length-Weight Relationship Modeling Applications in Aquatic Species Biomass Estimation Methods
type: item
description: Length-weight relationships represent fundamental biometric tools in aquatic ecology, providing essential methods for biomass estimation across diverse marine and freshwater species. These allometric relationships enable researchers to convert easily measured morphometric parameters into biomass values, facilitating population assessments and ecosystem monitoring. Contemporary approaches integrate traditional morphometric measurements with advanced technologies including metabarcoding, machine learning, and in situ imaging systems. The application of length-weight models spans multiple taxonomic groups, from zooplankton communities to large crustaceans and fish populations. Recent developments emphasize spatiotemporal modeling approaches that account for environmental variability and seasonal dynamics in aquatic systems. Machine learning algorithms have enhanced biomass estimation accuracy by incorporating multiple morphometric variables beyond simple length measurements. Metabarcoding techniques now enable species-specific biomass calculations from genetic material, revolutionizing plankton community assessments. The integration of these methodologies addresses critical challenges in aquatic biodiversity monitoring and fisheries management. This review synthesizes current applications of length-weight relationship modeling, examining methodological advances in biomass estimation techniques across aquatic ecosystems. The analysis encompasses traditional regression approaches, contemporary technological innovations, and emerging computational methods that improve estimation precision. Understanding these relationships remains crucial for sustainable fisheries management, ecosystem health assessment, and biodiversity conservation in aquatic environments.
subject: Articles
  topic: section
subject: length-weight relationships
  topic: keywords
subject: biomass estimation
  topic: keywords
subject: aquatic species
  topic: keywords
subject: allometric modeling
  topic: keywords
subject: morphometric analysis
  topic: keywords
subject: ecosystem monitoring
  topic: keywords
creator: Omar Siddiqui
rights: Copyright (c) 2025 Omar Siddiqui (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-08-26
publication date: 2025-08-26
starting page: 43
ending page: 52
doi: 10.71222/9nmep839

Migration Infrastructure Design Impacts on Riverine Crustacean Population Dynamics Studies
type: item
description: The proliferation of anthropogenic barriers in riverine systems has fundamentally altered the population dynamics of aquatic organisms, particularly migratory crustaceans. This paper examines how migration infrastructure design influences crustacean population dynamics through comprehensive analysis of barrier effects, passageway effectiveness, and population fragmentation patterns. River fragmentation disrupts natural migration patterns essential for crustacean life cycles, leading to population isolation and demographic changes. The construction of migration passageways represents a critical intervention strategy, yet their design parameters significantly influence success rates. This study synthesizes current understanding of barrier impacts on crustacean populations, evaluating infrastructure design principles that promote population connectivity. Key findings indicate that passageway design features including gradient, substrate composition, and flow characteristics directly influence migration success. Population dynamics modeling reveals that even partial connectivity restoration can substantially improve population stability in fragmented systems. The research highlights the importance of species-specific design considerations, particularly for commercially important species. Furthermore, the temporal aspects of migration infrastructure operation significantly affect population recruitment patterns. This comprehensive analysis provides essential guidance for designing effective migration infrastructure that supports sustainable crustacean populations while accommodating human development needs. The findings emphasize the critical role of adaptive management approaches in optimizing infrastructure performance for long-term population viability.
subject: Articles
  topic: section
subject: crustacean migration
  topic: keywords
subject: infrastructure design
  topic: keywords
subject: population dynamics
  topic: keywords
subject: river fragmentation
  topic: keywords
subject: barrier effects
  topic: keywords
subject: passageway effectiveness
  topic: keywords
creator: Liang Ming
rights: Copyright (c) 2025 Liang Ming (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-08-23
publication date: 2025-08-23
starting page: 33
ending page: 42
doi: 10.71222/spnht004

Carapace Measurement Techniques for Determining Growth Rates in Cultured Crustaceans
type: item
description: Accurate measurement of carapace dimensions is fundamental for assessing growth rates in cultured crustaceans, providing essential data for aquaculture management and biological research. This paper examines various carapace measurement methodologies employed in crustacean cultivation, analyzing their precision, applicability, and effectiveness across different species. Traditional morphometric approaches, including direct caliper measurements and photographic analysis, are evaluated alongside modern digital imaging techniques and automated measurement systems. The study reviews measurement protocols for major cultured species including mud crabs, red king crabs, lobsters, and freshwater crayfish, examining how environmental factors such as temperature influence growth patterns and measurement accuracy. Standardized measurement techniques are critical for establishing reliable length-weight relationships and determining size at maturity parameters essential for aquaculture optimization. The analysis reveals that carapace width measurements generally provide more consistent growth indicators than length measurements, particularly in brachyuran species. Temperature-dependent growth responses necessitate controlled measurement environments and standardized protocols to ensure data comparability across studies. Integration of morphometric data with molting frequency observations enhances growth rate calculations and provides comprehensive assessment tools for aquaculture practitioners. Recommendations include adoption of standardized measurement protocols, implementation of digital imaging systems for improved accuracy, and establishment of species-specific measurement guidelines to optimize growth monitoring in commercial crustacean culture operations.
subject: Articles
  topic: section
subject: carapace morphometry
  topic: keywords
subject: crustacean growth
  topic: keywords
subject: aquaculture measurements
  topic: keywords
subject: growth rates
  topic: keywords
subject: morphometric analysis
  topic: keywords
subject: digital imaging
  topic: keywords
creator: Ravi Mahadevan
rights: Copyright (c) 2025 Ravi Mahadevan (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-08-23
publication date: 2025-08-23
starting page: 23
ending page: 32
doi: 10.71222/6r29np53

Freshwater Ecosystem Management Strategies for Commercially Important Decapod Species Populations
type: item
description: Freshwater decapod crustaceans represent economically vital components of aquatic ecosystems worldwide, yet their populations face unprecedented threats from anthropogenic pressures including habitat degradation, pollution, and climate change. This paper examines comprehensive management strategies for commercially important freshwater decapod species, with particular emphasis on sustainable conservation approaches that balance ecological integrity with economic demands. The analysis encompasses ecosystem-based management frameworks, species-specific conservation protocols, and integrated multitrophic aquaculture systems that support both wild population recovery and commercial viability. Through examination of successful case studies from major river systems including the Yangtze River and Lake Victoria Basin, this study demonstrates that effective decapod management requires adaptive strategies incorporating habitat restoration, migration corridor establishment, water quality management, and community-based conservation initiatives. The research reveals that ecosystem-based approaches significantly outperform single-species management strategies in maintaining population stability and genetic diversity. Key findings indicate that migration passageway construction, nutrient cycling optimization, and stakeholder engagement represent critical components of successful conservation programs. The paper concludes that sustainable freshwater decapod management necessitates integrated approaches combining scientific research, traditional ecological knowledge, and adaptive management frameworks to address the complex challenges facing these commercially valuable species in rapidly changing aquatic environments.
subject: Articles
  topic: section
subject: freshwater decapods
  topic: keywords
subject: ecosystem management
  topic: keywords
subject: aquaculture
  topic: keywords
subject: biodiversity conservation
  topic: keywords
subject: sustainable fisheries
  topic: keywords
subject: crustacean ecology
  topic: keywords
creator: Nguyen Thi Lan
rights: Copyright (c) 2025 Nguyen Thi Lan (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-08-23
publication date: 2025-08-23
starting page: 13
ending page: 22
doi: 10.71222/cwb8p344

Crustacean Morphological Adaptations in Controlled Aquaculture Environments and Feeding Systems
type: item
description: Crustacean aquaculture has experienced unprecedented growth in recent decades, necessitating comprehensive understanding of morphological adaptations in controlled environments. This study examines the physiological and morphological responses of commercially important crustacean species to various aquaculture conditions, with particular emphasis on feeding systems, environmental stressors, and immune responses. The research reveals significant morphological plasticity in gill structures, digestive systems, and immune organs when crustaceans are subjected to different aquaculture conditions. Key findings indicate that controlled feeding regimens significantly influence carapace dimensions, antennae development, and hepatopancreas functionality. Environmental parameters such as water quality, stocking density, and nutritional composition directly correlate with morphological adaptations in respiratory systems and immune cell populations. The study demonstrates that crustaceans exhibit remarkable phenotypic flexibility, adapting their gill morphology by up to 35% in response to varying oxygen levels and developing enhanced hepatopancreas functionality under optimized feeding protocols. These adaptations have profound implications for aquaculture productivity, disease resistance, and sustainable production practices. Understanding these morphological responses enables the development of species-specific culture protocols that maximize growth efficiency while maintaining physiological health, ultimately contributing to the advancement of sustainable crustacean aquaculture practices worldwide.
subject: Articles
  topic: section
subject: crustacean morphology
  topic: keywords
subject: aquaculture adaptation
  topic: keywords
subject: feeding systems
  topic: keywords
subject: gill structure
  topic: keywords
subject: immune response
  topic: keywords
subject: physiological plasticity
  topic: keywords
creator: Jason McAllister
rights: Copyright (c) 2025 Jason McAllister (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-08-19
publication date: 2025-08-19
starting page: 1
ending page: 12
doi: 10.71222/va9pch67

Spatiotemporal Modeling of Soil Moisture in Humid Areas by Integrating Transformer Architecture and Remote Sensing Data
type: item
description: Soil moisture variation in humid regions presents high-frequency nonlinearity and is strongly influenced by surface conditions, making it difficult for traditional time series models to effectively capture long-range dependencies. In this study, a multimodal Transformer network architecture is proposed, integrating Sentinel-1 VV/VH radar data, MODIS NDVI, and meteorological variables to predict daily 0–10 cm soil moisture content at a 1 km² grid scale in Jiangning District, Nanjing. Data from 2021 were used for model training, and 2022 data were used for testing. Verified by in-situ measurements, the monthly average of 0–10 cm soil moisture in Jiangning during 2021 ranged from 0.20 to 0.32 m³/m³, with a standard deviation of 0.035 m³/m³, reflecting its high-frequency nonlinear characteristics. The model achieved an average RMSE of 0.022 m³/m³, which was lower than that of LSTM (RMSE = 0.029) and traditional SVR (RMSE = 0.034). The model's interpretability module (attention map) showed that vegetation cover and rainfall in the previous six days contributed 41.2% and 27.8%, respectively. This study provides an AI-based approach for modeling soil–vegetation–hydrology interactions driven by remote sensing data.
subject: Articles
  topic: section
subject: soil moisture prediction
  topic: keywords
subject: transformer
  topic: keywords
subject: remote sensing fusion
  topic: keywords
subject: humid region
  topic: keywords
subject: multimodal modeling
  topic: keywords
creator: Haojun Li
creator: Rebecca S. Miller
creator: Daniel T. Nguyen
creator: Christopher J. Roberts
rights: Copyright (c) 2025 Haojun Li, Rebecca S. Miller, Daniel T. Nguyen, Christopher J. Roberts (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-08-17
publication date: 2025-08-17
starting page: 96
ending page: 102
doi: 10.71222/t0pavh89

Leveraging NLP for Semantic and Numerical Inconsistency Detection in Tax Submissions
type: item
description: This study presents an innovative method for identifying tax fraud through the application of natural language processing (NLP) to uncover irregularities within tax documents. Departing from conventional approaches that rely primarily on numerical analysis, the proposed framework combines domain-specific BERT embeddings with bidirectional LSTM architectures to effectively capture nuanced contextual information. A hybrid ensemble architecture is developed to process both structured data and free-text components within tax returns, facilitating the identification of semantic associations among financial entities and exposing numerical inconsistencies. The system was evaluated on a dataset comprising 15,000 tax documents, of which 8.5% were identified as fraudulent. The proposed model achieved superior performance, with an F1-score of 0.868 and an AUC of 0.931 — marking a 7.6% enhancement over leading existing models. Detection effectiveness varied by document category: individual income tax filings yielded an F1-score of 0.889, outperforming business-related filings, which scored 0.818. Further examination reveals that semantic features are particularly effective for identifying fraud in corporate tax documents, while numerical coherence indicators are more significant for personal filings. Although the approach requires higher computational resources compared to conventional techniques, its capacity to detect complex fraud schemes — especially those that disguise manipulation within textual content while maintaining plausible numeric data — offers a significant improvement to current tax fraud detection systems.
subject: Articles
  topic: section
subject: tax compliance monitoring
  topic: keywords
subject: deep learning
  topic: keywords
subject: entity recognition
  topic: keywords
subject: financial document analysis
  topic: keywords
creator: Jiayu Liang
creator: Jiayan Fan
creator: Zhen Feng
creator: Jing Xin
rights: Copyright (c) 2025 Jiayu Liang, Jiayan Fan, Zhen Feng, Jing Xin (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-06-17
publication date: 2025-06-17
starting page: 87
ending page: 95
doi: 10.71222/0fmphc41

A Fault Diagnosis Method for the Charging and Discharging Water System of a Certain Ship Lift Based on Historical Data Analysis
type: item
description: This paper takes the hydraulic action of the discharge pipeline in the filling and discharging water system of a Certain ship lift as a case study. Based on analysis of operational data from different historical stages, a mathematical model is established to determine whether the equipment in the filling and discharging water system shows trends toward abnormal conditions. The proposed model will provide early warnings for equipment maintenance and reduce the frequency of timeout fault alarms for pipeline water extraction.
subject: Articles
  topic: section
subject: timeout of discharge pipeline water
  topic: keywords
subject: data analysis
  topic: keywords
subject: regression linear equation
  topic: keywords
creator: Binbin Zou
creator: Yansong Lei
creator: Gengbang Huang
creator: Lingzhi Yan
rights: Copyright (c) 2025 Binbin Zou, Yansong Lei, Gengbang Huang, Lingzhi Yan (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-06-11
publication date: 2025-06-11
starting page: 80
ending page: 86
doi: 10.71222/ygh4zc61